Topics in Grammatical Inference

José M. Sempere

ISBN 10: 3662483939 / ISBN 13: 9783662483930

Editore: Springer Berlin Heidelberg, 2016

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Druck auf Anfrage Neuware - Printed after ordering - This book explains advanced theoretical andapplication-related issues in grammatical inference, a research area inside theinductive inference paradigm for machine learning. The first three chapters ofthe book deal with issues regarding theoretical learning frameworks; the nextfour chapters focus on the main classes of formal languages according toChomsky's hierarchy, in particular regular and context-free languages; and thefinal chapter addresses the processing of biosequences.The topics chosen are of foundational interest withrelatively mature and established results, algorithms and conclusions. The bookwill be of value to researchers and graduate students in areas such astheoretical computer science, machine learning, computational linguistics, bioinformatics,and cognitive psychology who are engaged with the study of learning, especiallyof the structure underlying the concept to be learned. Some knowledge ofmathematics and theoretical computer science, including formal language theory,automata theory, formal grammars, and algorithmics, is a prerequisite forreading this book.

Codice articolo 9783662483930

Dati bibliografici

• Titolo: Topics in Grammatical Inference
• Editore: Springer Berlin Heidelberg
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3662483939
• ISBN 13: 9783662483930
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 21 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 21 millimetri di profondità
• Peso: 571 grammi

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Riassunto

This book explains advanced theoretical andapplication-related issues in grammatical inference, a research area inside theinductive inference paradigm for machine learning. The first three chapters ofthe book deal with issues regarding theoretical learning frameworks; the nextfour chapters focus on the main classes of formal languages according toChomsky's hierarchy, in particular regular and context-free languages; and thefinal chapter addresses the processing of biosequences.

 

The topics chosen are of foundational interest withrelatively mature and established results, algorithms and conclusions. The bookwill be of value to researchers and graduate students in areas such astheoretical computer science, machine learning, computational linguistics, bioinformatics,and cognitive psychology who are engaged with the study of learning, especiallyof the structure underlying the concept to be learned. Some knowledge ofmathematics and theoretical computer science, including formal language theory,automata theory, formal grammars, and algorithmics, is a prerequisite forreading this book.